@article {bnh-5056, title = {Limitations of high resolution satellite stereo imagery for estimating canopy height in Australian tropical savannas}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {75}, year = {2019}, month = {03/2019}, pages = {12}, chapter = {83}, abstract = {

Obtaining reliable measures of tree canopy height across large areas is a central element of forest inventory and carbon accounting. Recent years have seen an increased emphasis on the use of active sensors like Radar and airborne LiDAR (light detection and scanning) systems to estimate various 3D characteristics of canopy and crown structure that can be used as predictors of biomass. However, airborne LiDAR data are expensive to acquire, and not often readily available across large remote landscapes. In this study, we evaluated the potential of stereo imagery from commercially available Very High Resolution (VHR) satellites as an alternative for estimating canopy height variables in Australian tropical savannas, using a semi-global dense matching (SGM) image-based technique. We assessed and compared the completeness and vertical accuracy of extracted canopy height models (CHMs) from GeoEye 1 and WorldView 1 VHR satellite stereo pairs and summarised the factors influencing image matching effectiveness and quality.

Our results showed that stereo dense matching using the SGM technique severely underestimates tree presence and canopy height. The highest tree detection rates were achieved by using the near-infrared (NIR) band of GE1 (8{\textendash}9\%). WV1-GE1 cross-satellite (mixed) models did not improve the quality of extracted canopy heights. We consider these poor detection rates and height retrievals to result from: i) the clumping crown structure of the dominant Eucalyptus spp.; ii) their vertically oriented leaves (affecting the bidirectional reflectance distribution function); iii) image band radiometry and iv) wind induced crown movement affecting stereo-pair point matching. Our detailed analyses suggest that current commercially available VHR satellite data (0.5 m resolution) are not well suited to estimating canopy height variables, and therefore above ground biomass (AGB), in Eucalyptus dominated north Australian tropical savanna woodlands.

}, keywords = {canopy height, Satellite, Savanna, Stereo}, doi = {10.1016/j.jag.2018.10.021}, url = {https://www.sciencedirect.com/science/article/pii/S0303243418308390}, author = {Grigorijs Goldbergs and Stefan Maier and Shaun R Levick and Andrew C. Edwards} } @mastersthesis {bnh-5778, title = {Remote sensing of tree structure and biomass in north Australia mesic savanna}, volume = {Philosophy}, year = {2019}, month = {05/2019}, school = {Charles Darwin Unviersity}, type = {Doctorate}, address = {Melbourne}, abstract = {

The quantification of Above Ground Biomass (AGB) plays a major role in issues related to greenhouse gas emissions and carbon sequestration, pertaining to global warming and the effects of climate change. In Eucalyptus miniata/tetrodonta dominated open-forest in Australia{\textquoteright}s northern tropical savanna, AGB mapping is challenging, due to the complex structure of the canopy stand, highly dynamic woody cover, vast spatial extent, vulnerability to climatic effects and the impacts of extensive fire.\ 

Remotely sensed data allow for the mapping, quantification and monitoring of AGB at various scales. Quantification of AGB in tropical savanna by common medium and coarse spatial resolution optical sensor data is inappropriate to monitor finer-grained ecological processes responsible for measuring carbon stocks at an individual tree level and are limited in detecting vertical vegetation structure. There is limited research on the utility of airborne LiDAR (Light Detection and Ranging) and alternative high resolution (\< 0.5 m) remote sensing tools for Australian savanna structural assessment. The main goal of this research is to evaluate the efficiency of small footprint airborne LiDAR and determine whether Unmanned Aerial Systems (UAS) and Very High Resolution (VHR) satellite stereo remote sensing data can be used to extract tree biophysical and vertical structural parameters for the purposes of accurately estimating biomass stocks in Australian mesic savannas.\ 

This study utilized a two-phase LiDAR analysis procedure integrating both Individual Tree Detection (ITC) and Area-Based Approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. Regression analysis was applied on remote sensing data to develop biomass estimation models based on tree height allometry. This study demonstrated that where field-plot data are spatially limited, it is possible to use a hierarchical integration approach based on AGB uncertainty calculation and calibration to upscale AGB estimates from individual trees to broader landscapes.

}, keywords = {biomass, greenhouse gas, Lidar, northern Australia, remote sensing, tree structure}, url = {https://researchers.cdu.edu.au/en/studentTheses/remote-sensing-of-tree-structure-and-biomass-in-north-australia-m}, author = {Grigorijs Goldbergs} } @article {bnh-4424, title = {Efficiency of Individual Tree Detection Approaches Based on Light-Weight and Low-Cost UAS Imagery in Australian Savannas}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {01/2018}, abstract = {
The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds{\textemdash}obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera{\textemdash}can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70\%) for dominant and co-dominant trees, but yielded low detection rates (\<35\%) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.
}, keywords = {biomass, canopy height, low-cost UAS, segmentation, single tree detection, structure from motion}, doi = {10.3390/rs10020161 }, url = {http://www.mdpi.com/2072-4292/10/2/161/htm}, author = {Grigorijs Goldbergs and Stefan Maier and Shaun R Levick and Andrew C. Edwards} } @article {bnh-4335, title = {Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR}, journal = {Remote Sensing of Environment}, volume = {205}, year = {2018}, month = {02/2018}, pages = {141-150}, chapter = {141}, abstract = {

Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2\ =\ 0.754, RMSE\ =\ 90\ kg), and that 63\% of individual trees crowns (ITC) could be accurately delineated with a\ canopy maximaapproach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4\ ha plots: R2\ =\ 0.86, RMSE\ =\ 3.4\ Mg\ ha-\ 1; and 1\ ha plots: R2\ =\ 0.83, RMSE\ =\ 4.0\ Mg\ ha-\ 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.

}, doi = {10.1016/j.rse.2017.11.010}, url = {https://www.sciencedirect.com/science/article/pii/S0034425717305357}, author = {Grigorijs Goldbergs and Shaun R Levick and Michael Lawes and Andrew C. Edwards} }